{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# White Christmas\n",
    "\n",
    "## HadCET\n",
    "The [HadCET](http://www.metoffice.gov.uk/hadobs/hadcet/) data is \"the longest available instrumental record of temperature in the world\", currently available from the UK Met Office. It provides the daily mean temperature for the centre of England since 1772.\n",
    "\n",
    "From that table you can see that the mean daily temperature for 1st January 2001 was recorded as 40 meaning 4.0°C, the temperature for 2nd January 2001 has 77 which translates to 7.7°C\n",
    "\n",
    "The February temperatures are in the m2 column, notice that -999 has been entered for invalid cells such as 31 Feb 2001.\n",
    "\n",
    "```\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "| yr   | dy |  m1 |  m2 |  m3 |  m4 |  m5 |  m6 |  m7 |  m8 |  m9 | m10 | m11 | m12 |\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "| 2001 |  1 |  40 |  11 |   9 | 112 |  87 | 131 | 185 | 180 | 150 | 151 |  91 |  98 |\n",
    "| 2001 |  2 |  77 |  32 |  -4 | 114 |  99 | 126 | 207 | 163 | 168 | 143 |  99 |  29 |\n",
    "| 2001 |  3 |  52 |  66 |  -3 |  84 | 105 | 100 | 209 | 166 | 153 | 142 |  87 |  55 |\n",
    "| 2001 |  4 |  50 |  57 |   5 |  69 |  72 | 130 | 219 | 151 | 134 | 139 |  90 |  73 |\n",
    "| 2001 |  5 |  47 |  68 |  12 |  71 |  82 | 136 | 217 | 161 | 139 | 159 |  75 |  82 |\n",
    "| 2001 |  6 |  43 |  83 |  57 |  90 |  76 | 135 | 201 | 173 | 141 | 142 |  95 |  46 |\n",
    "| 2001 |  7 |  49 |  85 |  86 |  69 |  90 | 110 | 185 | 161 | 153 | 128 | 112 |  62 |\n",
    "| 2001 |  8 |  51 |  34 |  87 |  71 |  93 | 102 | 163 | 155 | 147 | 127 |  50 |  40 |\n",
    "| 2001 |  9 |  22 |  21 |  90 | 101 | 102 | 101 | 157 | 141 | 121 | 132 |  33 |  16 |\n",
    "| 2001 | 10 |  23 |  51 | 102 |  96 | 147 | 110 | 160 | 152 | 119 | 125 |  44 |  19 |\n",
    "| 2001 | 11 |  29 |  81 | 101 |  72 | 172 | 106 | 141 | 152 | 142 | 150 |  78 |  20 |\n",
    "| 2001 | 12 |  40 |  79 |  60 |  74 | 168 | 138 | 144 | 171 | 142 | 153 | 101 |  46 |\n",
    "| 2001 | 13 |  31 |  40 |  57 |  57 | 165 | 148 | 143 | 195 | 124 | 160 |  53 |  47 |\n",
    "| 2001 | 14 |  24 |  35 |  64 |  75 | 118 | 144 | 132 | 205 | 124 | 155 |  40 |  15 |\n",
    "| 2001 | 15 |  18 |  44 |  53 |  91 | 120 | 153 | 127 | 210 | 129 | 154 |  43 |  13 |\n",
    "| 2001 | 16 | -12 |  46 |  54 |  73 | 128 | 138 | 133 | 176 | 125 | 117 |  55 |  23 |\n",
    "| 2001 | 17 |  -9 |  21 |  26 |  52 |  89 | 123 | 143 | 157 | 109 | 138 |  83 |  20 |\n",
    "| 2001 | 18 |  -5 |   3 |  28 |  44 | 101 | 128 | 149 | 167 | 124 | 147 |  73 |  38 |\n",
    "| 2001 | 19 |  -8 |  26 |  20 |  54 | 119 | 160 | 137 | 181 | 129 | 113 |  63 |  48 |\n",
    "| 2001 | 20 |  -5 |  36 |  22 |  54 | 130 | 156 | 146 | 169 | 138 | 135 |  67 |  26 |\n",
    "| 2001 | 21 |  13 |  62 |  26 |  52 | 129 | 140 | 153 | 159 | 116 | 104 |  96 |  41 |\n",
    "| 2001 | 22 |  45 |  73 |  60 |  64 | 131 | 139 | 169 | 179 | 119 | 118 |  91 |  12 |\n",
    "| 2001 | 23 |  69 |  49 |  77 |  84 | 150 | 160 | 170 | 179 | 103 | 137 |  48 |  18 |\n",
    "| 2001 | 24 |  69 |  27 |  68 |  68 | 157 | 173 | 155 | 191 | 127 | 130 |  68 |  34 |\n",
    "| 2001 | 25 |  48 |  14 |  49 |  85 | 137 | 191 | 181 | 193 | 123 | 124 | 115 |  45 |\n",
    "| 2001 | 26 |  47 |  27 |  38 |  90 | 157 | 219 | 184 | 171 | 112 | 124 |  51 |  27 |\n",
    "| 2001 | 27 |  40 |  27 |  50 |  84 | 160 | 187 | 194 | 152 | 145 | 122 |  48 |  40 |\n",
    "| 2001 | 28 |  11 |  23 |  70 |  99 | 176 | 170 | 206 | 143 | 166 | 116 |  76 |  43 |\n",
    "| 2001 | 29 |  20 |-999 |  66 |  76 | 160 | 177 | 230 | 149 | 168 | 106 |  96 |  18 |\n",
    "| 2001 | 30 |  25 |-999 |  83 |  80 | 149 | 170 | 201 | 154 | 141 | 140 | 124 |  15 |\n",
    "| 2001 | 31 |  40 |-999 | 100 |-999 | 130 |-999 | 192 | 146 |-999 | 101 |-999 |  -4 |\n",
    "| 2002 |  1 | -18 |  86 |  45 | 113 |  93 | 143 | 131 | 161 | 126 | 145 | 114 |  87 |\n",
    "| 2002 |  2 | -18 | 109 |  31 | 100 |  84 | 162 | 135 | 156 | 140 | 156 | 114 |  83 |\n",
    "+------+----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ·········\n"
     ]
    }
   ],
   "source": [
    "# Prerequesites\n",
    "import getpass\n",
    "%load_ext sql\n",
    "pwd = getpass.getpass()\n",
    "# %sql mysql+pymysql://root:$pwd@localhost:3306/sqlzoo\n",
    "%sql postgresql://postgres:$pwd@localhost/sqlzoo\n",
    "%config SqlMagic.displaylimit = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Days, Months and Years\n",
    "The units are 10th of a degree Celcius. The columns are yr and dy for year and day of month. The next twelve columns are for January through to December.\n",
    "\n",
    "**Show the average daily temperature for August 10th 1964**\n",
    "\n",
    "```sql\n",
    "show create table hadcet\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost/sqlzoo\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>m8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>139</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(139,)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT m8 FROM hadcet\n",
    "  WHERE yr=1964 AND dy=10;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. preteen Dickens\n",
    "\n",
    "Charles Dickens is said to be responsible for the tradition of expecting snow at Christmas [Daily Telegraph](http://www.telegraph.co.uk/topics/christmas/3933091/Dreaming-of-a-white-Christmas-Blame-the-nostalgia-of-Charles-Dickens-snowy-childhood.html). Show the temperature on Christmas day (25th December) for each year of his childhood. He was born in February 1812 - so he was 1 (more or less) in December 1812.\n",
    "\n",
    "**Show the twelve temperatures.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost/sqlzoo\n",
      "13 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>age</th>\n",
       "        <th>m12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>5</td>\n",
       "        <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>6</td>\n",
       "        <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7</td>\n",
       "        <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>8</td>\n",
       "        <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>9</td>\n",
       "        <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>10</td>\n",
       "        <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>11</td>\n",
       "        <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>12</td>\n",
       "        <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>13</td>\n",
       "        <td>104</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 0),\n",
       " (2, 81),\n",
       " (3, 11),\n",
       " (4, -8),\n",
       " (5, 30),\n",
       " (6, -4),\n",
       " (7, 10),\n",
       " (8, -8),\n",
       " (9, -3),\n",
       " (10, 35),\n",
       " (11, 5),\n",
       " (12, 78),\n",
       " (13, 104)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT yr-1811 AS age, m12 FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 AND 1812+12 AND dy=25;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Minimum Temperature Before Christmas\n",
    "\n",
    "We declare a White Christmas if there was a day with an average temperature below zero between 21st and 25th of December.\n",
    "\n",
    "**For each age 1-12 show which years were a White Christmas. Show 'White Christmas' or 'No snow' for each age.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost/sqlzoo\n",
      "12 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>age</th>\n",
       "        <th>lbl</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>2</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>3</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>4</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>5</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>6</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>7</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>8</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>9</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>10</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>11</td>\n",
       "        <td>White Christmas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>12</td>\n",
       "        <td>No snow</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1, 'No snow'),\n",
       " (2, 'No snow'),\n",
       " (3, 'White Christmas'),\n",
       " (4, 'White Christmas'),\n",
       " (5, 'White Christmas'),\n",
       " (6, 'White Christmas'),\n",
       " (7, 'White Christmas'),\n",
       " (8, 'White Christmas'),\n",
       " (9, 'White Christmas'),\n",
       " (10, 'No snow'),\n",
       " (11, 'White Christmas'),\n",
       " (12, 'No snow')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT yr-1811 AS age, CASE WHEN MIN(m12)<0 THEN 'White Christmas' ELSE 'No snow' END lbl\n",
    "  FROM hadcet\n",
    "    WHERE yr BETWEEN 1812 AND 1812+11 AND dy BETWEEN 21 AND 25\n",
    "    AND m12 <> -999\n",
    "    GROUP BY yr\n",
    "    ORDER BY yr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. White Christmas Count\n",
    "\n",
    "A person's White Christmas Count (wcc) is the number of White Christmases they were exposed to as a child (between 3 and 12 inclusive assuming they were born at the beginning of the year and were about 1 year old on their first Christmas).\n",
    "\n",
    "Charles Dickens's wcc was 8.\n",
    "\n",
    "**List all the years and the wcc for children born in each year of the data set. Only show years where the wcc was at least 7.**\n",
    "\n",
    "```sql\n",
    "SELECT yr-1811 as age FROM hadcet\n",
    "  WHERE yr BETWEEN 1812 and 1812+12 AND dy=25\n",
    "```\n",
    "\n",
    "```\n",
    "+------+-----+\n",
    "| yob  | wcc |\n",
    "+------+-----+\n",
    "| 1805 |   7 |\n",
    "| 1806 |   7 |\n",
    "| 1807 |   7 |\n",
    "| 1808 |   8 |\n",
    "| 1809 |   9 |\n",
    "| 1810 |   8 |\n",
    "| 1811 |   8 |\n",
    "| 1812 |   8 |\n",
    "| 1813 |   7 |\n",
    "+------+-----+\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost/sqlzoo\n",
      "5 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>yr</th>\n",
       "        <th>wcc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1810</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1811</td>\n",
       "        <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1813</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1809</td>\n",
       "        <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1812</td>\n",
       "        <td>8</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(1810, 7), (1811, 8), (1813, 7), (1809, 7), (1812, 8)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "SELECT x.yr, COUNT(y.yr) wcc\n",
    "FROM (SELECT DISTINCT yr FROM hadcet) x LEFT JOIN (\n",
    "   SELECT yr FROM hadcet WHERE dy between 21 AND 25 AND m12 <> -999\n",
    "    GROUP BY yr \n",
    "    HAVING SUM(CASE WHEN m12 < 0 THEN 1 ELSE 0 END) > 0\n",
    ") y ON y.yr >= x.yr + 2 AND y.yr <= x.yr + 11\n",
    "GROUP BY x.yr\n",
    "HAVING COUNT(y.yr) >= 7;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Climate Change\n",
    "\n",
    "Here are the average temperatures for August by decade. You decide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * postgresql://postgres:***@localhost/sqlzoo\n",
      "25 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>decade</th>\n",
       "        <th>avg_temp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1770</td>\n",
       "        <td>163.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1780</td>\n",
       "        <td>155.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1790</td>\n",
       "        <td>157.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1800</td>\n",
       "        <td>163.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1810</td>\n",
       "        <td>148.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1820</td>\n",
       "        <td>154.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1830</td>\n",
       "        <td>153.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1840</td>\n",
       "        <td>150.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1850</td>\n",
       "        <td>157.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1860</td>\n",
       "        <td>152.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1870</td>\n",
       "        <td>157.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1880</td>\n",
       "        <td>151.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1890</td>\n",
       "        <td>155.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1900</td>\n",
       "        <td>150.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1910</td>\n",
       "        <td>156.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1920</td>\n",
       "        <td>149.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1930</td>\n",
       "        <td>162.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1940</td>\n",
       "        <td>161.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1950</td>\n",
       "        <td>156.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>1960</td>\n",
       "        <td>151.84</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<span style=\"font-style:italic;text-align:center;\">25 rows, truncated to displaylimit of 20</span>"
      ],
      "text/plain": [
       "[(Decimal('1770'), Decimal('163.16')),\n",
       " (Decimal('1780'), Decimal('155.46')),\n",
       " (Decimal('1790'), Decimal('157.57')),\n",
       " (Decimal('1800'), Decimal('163.88')),\n",
       " (Decimal('1810'), Decimal('148.09')),\n",
       " (Decimal('1820'), Decimal('154.14')),\n",
       " (Decimal('1830'), Decimal('153.44')),\n",
       " (Decimal('1840'), Decimal('150.96')),\n",
       " (Decimal('1850'), Decimal('157.73')),\n",
       " (Decimal('1860'), Decimal('152.23')),\n",
       " (Decimal('1870'), Decimal('157.23')),\n",
       " (Decimal('1880'), Decimal('151.56')),\n",
       " (Decimal('1890'), Decimal('155.58')),\n",
       " (Decimal('1900'), Decimal('150.08')),\n",
       " (Decimal('1910'), Decimal('156.40')),\n",
       " (Decimal('1920'), Decimal('149.92')),\n",
       " (Decimal('1930'), Decimal('162.53')),\n",
       " (Decimal('1940'), Decimal('161.07')),\n",
       " (Decimal('1950'), Decimal('156.98')),\n",
       " (Decimal('1960'), Decimal('151.84')),\n",
       " (Decimal('1970'), Decimal('159.85')),\n",
       " (Decimal('1980'), Decimal('158.50')),\n",
       " (Decimal('1990'), Decimal('167.70')),\n",
       " (Decimal('2000'), Decimal('166.79')),\n",
       " (Decimal('2010'), Decimal('160.07'))]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = %sql SELECT DISTINCT ROUND(yr/10,0)*10 AS decade, \\\n",
    "  ROUND(AVG(m8) OVER(PARTITION BY ROUND(yr/10,0)*10), 2) avg_temp \\\n",
    "  FROM hadcet \\\n",
    "  WHERE m8 <> -999 \\\n",
    "  ORDER BY ROUND(yr/10,0)*10;\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.DataFrame(df)\n",
    "df.columns=['decade', 'avg_temp']\n",
    "df['avg_temp'] = df['avg_temp']/10\n",
    "plt.plot(df['decade'], df['avg_temp'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
